Genetic diversity of hepatitis B virus quasispecies in different biological compartments reveals distinct genotypes

Carregando...
Imagem de Miniatura
Citações na Scopus
0
Tipo de produção
article
Data de publicação
2023
Título da Revista
ISSN da Revista
Título do Volume
Editora
NATURE PORTFOLIO
Autores
LAGO, Barbara Vieira do
BEZERRA, Cristianne Sousa
MOREIRA, Daniel Andrade
PARENTE, Thiago Estevam
PORTILHO, Moyra Machado
PESSOA, Rodrigo
VILLAR, Livia Melo
Citação
SCIENTIFIC REPORTS, v.13, n.1, article ID 17023, 10p, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
The selection pressure imposed by the host immune system impacts hepatitis B virus (HBV) quasispecies variability. This study evaluates HBV genetic diversity in different biological fluids. Twenty paired serum, oral fluid, and DBS samples from chronic HBV carriers were analyzed using both Sanger and next generation sequencing (NGS). The mean HBV viral load in serum was 5.19 +/- 4.3 log IU/mL (median 5.29, IQR 3.01-7.93). Genotype distribution was: HBV/A1 55% (11/20), A2 15% (3/20), D3 10% (2/20), F2 15% (3/20), and F4 5% (1/20). Genotype agreement between serum and oral fluid was 100% (genetic distances 0.0-0.006), while that between serum and DBS was 80% (genetic distances 0.0-0.115). Two individuals presented discordant genotypes in serum and DBS. Minor population analysis revealed a mixed population. All samples displayed mutations in polymerase and/or surface genes. Major population analysis of the polymerase pointed to positions H122 and M129 as the most polymorphic (>= 75% variability), followed by V163 (55%) and I253 (50%). Neither Sanger nor NGS detected any antiviral primary resistance mutations in the major populations. Minor population analysis, however, demonstrated the rtM204I resistance mutation in all individuals, ranging from 2.8 to 7.5% in serum, 2.5 to 6.3% in oral fluid, and 3.6 to 7.2% in DBS. This study demonstrated that different fluids can be used to assess HBV diversity, nonetheless, genotypic differences according to biological compartments can be observed.
Palavras-chave
Referências
  1. Amponsah-Dacosta E, 2016, INFECT GENET EVOL, V43, P232, DOI 10.1016/j.meegid.2016.05.035
  2. Araujo NM, 2015, INFECT GENET EVOL, V36, P500, DOI 10.1016/j.meegid.2015.08.024
  3. Astrovskaya I, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-S6-S1
  4. Bezerra CS, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-05264-1
  5. Bezerra CS, 2021, MICROBIOLOGYOPEN, V10, DOI 10.1002/mbo3.1161
  6. Bolger AM, 2014, BIOINFORMATICS, V30, P2114, DOI 10.1093/bioinformatics/btu170
  7. Cao L, 2014, J VIROL, V88, P8656, DOI 10.1128/JVI.01123-14
  8. Castellano S, 2021, GENES-BASEL, V12, DOI 10.3390/genes12030384
  9. Cavaretto LSP, 2018, J MED VIROL, V90, P277, DOI 10.1002/jmv.24940
  10. Coffin CS, 2011, J VIRAL HEPATITIS, V18, P415, DOI 10.1111/j.1365-2893.2010.01321.x
  11. Datta S, 2009, J VIROL, V83, P9983, DOI 10.1128/JVI.01905-08
  12. Ewels P, 2016, BIOINFORMATICS, V32, P3047, DOI 10.1093/bioinformatics/btw354
  13. Chachá SGF, 2017, BRAZ J INFECT DIS, V21, P424, DOI 10.1016/j.bjid.2017.01.011
  14. Kang YY, 2018, VIRUS RES, V257, P33, DOI 10.1016/j.virusres.2018.08.019
  15. Knyazev S, 2021, NUCLEIC ACIDS RES, V49, DOI 10.1093/nar/gkab576
  16. Kramvis A, 2014, INTERVIROLOGY, V57, P141, DOI 10.1159/000360947
  17. Kumar S, 2016, MOL BIOL EVOL, V33, P1870, DOI [10.1093/molbev/msw054, 10.1093/molbev/msv279]
  18. Lago BV, 2019, VIRUSES-BASEL, V11, DOI 10.3390/v11090860
  19. Lago BV, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0105317
  20. Lampe E, 2017, J GEN VIROL, V98, P1389, DOI 10.1099/jgv.0.000789
  21. Laura M, 2019, INFECT GENET EVOL, V71, P91, DOI 10.1016/j.meegid.2019.03.020
  22. Li H, 2009, BIOINFORMATICS, V25, P2078, DOI 10.1093/bioinformatics/btp352
  23. Li H, 2009, BIOINFORMATICS, V25, P1754, DOI 10.1093/bioinformatics/btp324
  24. Lin SR, 2021, JHEP REP, V3, DOI 10.1016/j.jhepr.2021.100254
  25. Ma Q, 2012, J MED VIROL, V84, P198, DOI 10.1002/jmv.23183
  26. Mallory MA, 2011, J VIROL METHODS, V177, P31, DOI 10.1016/j.jviromet.2011.06.009
  27. Mello FCA, 2007, BMC MICROBIOL, V7, DOI 10.1186/1471-2180-7-103
  28. Mina T, 2015, J CLIN VIROL, V71, P93, DOI 10.1016/j.jcv.2015.08.010
  29. Pessôa R, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0152499
  30. Pessôa R, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0093374
  31. Portilho M, 2017, ORAL DIS, V23, P1072, DOI 10.1111/odi.12692
  32. Portilho MM, 2021, ARCH VIROL, V166, P2435, DOI 10.1007/s00705-021-05122-x
  33. Portilho MM, 2018, J VIROL METHODS, V256, P100, DOI 10.1016/j.jviromet.2018.03.001
  34. Portilho MM, 2012, J MED MICROBIOL, V61, P844, DOI 10.1099/jmm.0.040238-0
  35. Puche ML, 2016, INVEST CLIN, V57, P38
  36. Reis LMW, 2011, J MED VIROL, V83, P2103, DOI 10.1002/jmv.22246
  37. Stirling C, 2010, BMC HEALTH SERV RES, V10, DOI 10.1186/1472-6963-10-122
  38. Tai DI, 2001, J GASTROENTEROL, V36, P200, DOI 10.1007/s005350170130
  39. Tarasov A, 2015, BIOINFORMATICS, V31, P2032, DOI 10.1093/bioinformatics/btv098
  40. Tong SP, 2016, J HEPATOL, V64, pS4, DOI 10.1016/j.jhep.2016.01.027
  41. Wickham H., 2016, ggplot2: Elegant Graphics for Data Analysis (R package version 3.3.5 ed.), DOI 10.1007/978-3-319-24277-4
  42. Wolf JM, 2021, INFECT GENET EVOL, V93, DOI 10.1016/j.meegid.2021.104936
  43. Wu CC, 2010, J GEN VIROL, V91, P483, DOI 10.1099/vir.0.012740-0
  44. Wu IC, 2018, J BIOMED SCI, V25, DOI 10.1186/s12929-018-0442-4
  45. Yu DM, 2014, J HEPATOL, V60, P515, DOI 10.1016/j.jhep.2013.11.004